{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "include(\"ALBreIF/ALBreIF.jl\")\n",
    "include(\"BPGF/BPGF.jl\")\n",
    "using .ALBreIF\n",
    "using .BPGF\n",
    "\n",
    "using SparseArrays"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0 0.0 … 0.0 0.0; 0.011198672726755647 0.0 … 0.0 0.0; … ; 0.0 0.0 … 0.0 0.0; 0.0 0.12586574285282642 … 0.0 0.0770393527336861], [0.009538373201359918 0.04948015174102737 … 0.013842199220397667 0.025873116229185034; 0.04421341627004829 0.02005836797386442 … 0.03432247697194731 0.014237574219150999; … ; 0.06179499737531647 0.01033635354850184 … 0.010770232396808018 0.033393723791336885; 0.050461122494293194 0.03634203731044325 … 0.029619629501347205 0.022305265292412887])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = rand(Float64, 200, 200)\n",
    "A = BPGF.normalize!(A)\n",
    "X, Y = BPGF.randinit(A, 10, 0.2, 1.0) # initialize the fatorization matrices"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Configure parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "false"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ρ₀ = 0.8 # step size parameter\n",
    "μ₀ = 0.0001 # regularization cofficient\n",
    "μ = 0.0 # another regularization coefficient\n",
    "rtime = 300 # runtime\n",
    "version = false"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Main.ALBreIF.Result{Float64}([0.038045444399968376 0.03678783502380561 … 0.03980247899128682 0.0; 0.03311022771813535 0.0 … 0.017070706101793037 0.06038552090732544; … ; 0.0 0.07256612280249114 … 0.05137443198968557 0.0402966092888501; 0.0 0.06060613598713868 … 0.030211918670524862 0.07302293484515517], [0.015081654786542364 0.009129408894043038 … 0.02524778507843602 0.014305438479862233; 0.032464065051777076 0.0056340866668545125 … 0.0005584329329583087 0.015078358531068023; … ; 0.018659563802473394 0.015222507257176414 … 0.014851382741914991 0.0061999849267714196; 0.0 0.021004316507580202 … 0.031243932685061343 0.023641132174535784], 200331, true, [0.0 1.000000662640958; 0.12000012397766113 0.9902519858554694; … ; 0.0 0.0; 0.0 0.0])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X₀ = copy(X);\n",
    "Y₀ = copy(Y);\n",
    "r₀ = BPGF.solve!(BPGF.BPG{Float64}(runtime=rtime,\n",
    "                verbose=version,\n",
    "                ρ=0.02,\n",
    "                μ₁=μ₀,\n",
    "                μ₂=μ), A, X₀, Y₀)\n",
    "\n",
    "X₁ = copy(X);\n",
    "Y₁ = copy(Y);\n",
    "r₁ = BPGF.solve!(BPGF.BBPG{Float64}(runtime=rtime,\n",
    "                verbose=version,\n",
    "                ρ=0.02,\n",
    "                μ₁=μ₀,\n",
    "                μ₂=μ), A, X₁, Y₁)\n",
    "\n",
    "X₂ = copy(X);\n",
    "Y₂ = copy(Y);\n",
    "r₂ = ALBreIF.solve!(ALBreIF.ALBreI{Float64}(runtime=rtime,\n",
    "                verbose=version,\n",
    "                ρ=0.1,\n",
    "                μ₁=μ₀,\n",
    "                μ₂=μ), A, X₂, Y₂)\n",
    "\n",
    "X₃ = copy(X);\n",
    "Y₃ = copy(Y);\n",
    "r₃ = ALBreIF.solve!(ALBreIF.ABLBreI{Float64}(runtime=rtime,\n",
    "                verbose=version,\n",
    "                ρ=0.1,\n",
    "                μ₁=μ₀,\n",
    "                μ₂=μ), A, X₃, Y₃)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Store experimental results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200331-element Vector{Float64}:\n",
       " 1.000000662640958\n",
       " 0.9902519858554694\n",
       " 0.9811559296535399\n",
       " 0.9720364388043897\n",
       " 0.9629772586633365\n",
       " 0.9547084463052424\n",
       " 0.946162642204851\n",
       " 0.9396001224495881\n",
       " 0.9325298657413127\n",
       " 0.923001254060488\n",
       " ⋮\n",
       " 0.2113334485146877\n",
       " 0.2113334476142954\n",
       " 0.2113334458035021\n",
       " 0.21133344419032055\n",
       " 0.21133344381866775\n",
       " 0.2113334426837599\n",
       " 0.21133344160988352\n",
       " 0.2113334406227275\n",
       " 0.2113334402403665"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stop₀ = r₀.niters\n",
    "pic₀ = r₀.objvalue\n",
    "rt₀ = pic₀[1:stop₀, 1]\n",
    "obj₀ = pic₀[1:stop₀, 2]\n",
    "\n",
    "stop₁ = r₁.niters\n",
    "pic₁ = r₁.objvalue\n",
    "rt₁ = pic₁[1:stop₁, 1]\n",
    "obj₁ = pic₁[1:stop₁, 2]\n",
    "\n",
    "stop₂ = r₂.niters\n",
    "pic₂ = r₂.objvalue\n",
    "rt₂ = pic₂[1:stop₂, 1]\n",
    "obj₂ = pic₂[1:stop₂, 2]\n",
    "\n",
    "stop₃ = r₃.niters\n",
    "pic₃ = r₃.objvalue\n",
    "rt₃ = pic₃[1:stop₃, 1]\n",
    "obj₃ = pic₃[1:stop₃, 2]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Draw Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using CairoMakie\n",
    "using LaTeXStrings\n",
    "using Colors\n",
    "using AlgebraOfGraphics\n",
    "CairoMakie.activate!()\n",
    "function speed()\n",
    "        lines(rt₀, obj₀; color=\"#389826\", linewidth=2, linestyle=:solid,\n",
    "                label=\"BPG\",\n",
    "                figure=(; figure_padding=50, resolution=(1200, 800), font=\"sans\",\n",
    "                        backgroundcolor=:white, fontsize=32),\n",
    "                axis=(; title = L\"$\\text{Synthesis Matrix-Regularization} (\\mu_1=10^{-4})$\",\n",
    "                        xlabel=\"Time(sec)\", ylabel=L\"\\Vert A-XY\\Vert_F\",\n",
    "                        yscale=log10,\n",
    "                        xgridstyle=:dash, ygridstyle=:dash,\n",
    "                        topspinevisible = false, rightspinevisible =false))\n",
    "        lines!(rt₁, obj₁; color=\"blue\", linewidth=2, linestyle=:solid,\n",
    "                label=\"BBPG\")\n",
    "        lines!(rt₂, obj₂; color=\"#FFC633\", linewidth=2, linestyle=:solid,\n",
    "                label=\"ALBreI\")\n",
    "        lines!(rt₃, obj₃; color=\"#CB3C33\", linewidth=2, linestyle=:solid,\n",
    "                label=\"ABLBreI\")\n",
    "        limits!(0, rtime, 10^(-0.8), 1)\n",
    "        axislegend(merge=true)\n",
    "        current_figure()\n",
    "end\n",
    "\n",
    "with_theme(speed, theme=theme_dark())\n",
    "speed()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CairoMakie.Screen{IMAGE}\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "save(\"plot/synthesis_data_1.png\", speed())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "find_closest (generic function with 1 method)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "function find_closest(value, arr)\n",
    "    min_diff = abs(arr[1] - value)\n",
    "    closest_index = 1\n",
    "    \n",
    "    for i in 2:length(arr)\n",
    "        diff = abs(arr[i] - value)\n",
    "        if diff < min_diff\n",
    "            min_diff = diff\n",
    "            closest_index = i\n",
    "        end\n",
    "    end\n",
    "    \n",
    "    return closest_index\n",
    "end\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.22966409258899864\n",
      "0.22174233257609713\n",
      "0.21465419881886483\n",
      "0.2113334406227275"
     ]
    }
   ],
   "source": [
    "print(obj₃[find_closest(15, rt₃)])\n",
    "print(\"\\n\")\n",
    "print(obj₃[find_closest(30, rt₃)])\n",
    "print(\"\\n\")\n",
    "print(obj₃[find_closest(60, rt₃)])\n",
    "print(\"\\n\")\n",
    "print(obj₃[find_closest(300, rt₃)])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.8.3",
   "language": "julia",
   "name": "julia-1.8"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.8.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
